The following pages link to Stochastic gradient boosting. (Q5958470):
Displaying 50 items.
- Boosted multivariate trees for longitudinal data (Q113262) (← links)
- Greedy function approximation: A gradient boosting machine. (Q127532) (← links)
- Power system parameters forecasting using Hilbert-Huang transform and machine learning (Q278553) (← links)
- CRM in social media: predicting increases in Facebook usage frequency (Q319329) (← links)
- Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research (Q319944) (← links)
- An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market (Q320966) (← links)
- Conditional validity of inductive conformal predictors (Q374136) (← links)
- Classification of nodal pockets in many-electron wave functions via machine learning (Q445427) (← links)
- Comment on: Boosting algorithms: regularization, prediction and model fitting (Q449783) (← links)
- Rejoinder: Boosting algorithms: regularization, prediction and model fitting (Q449785) (← links)
- Forecasting nonstationary time series based on Hilbert-Huang transform and machine learning (Q463396) (← links)
- Variable selection for BART: an application to gene regulation (Q484051) (← links)
- Small area estimation of the homeless in Los Angeles: an application of cost-sensitive stochastic gradient boosting (Q614137) (← links)
- Machine learning feature selection methods for landslide susceptibility mapping (Q745733) (← links)
- Soft-max boosting (Q747255) (← links)
- A theoretical understanding of self-paced learning (Q778415) (← links)
- Cultural consensus theory for the evaluation of patients' mental health scores in forensic psychiatric hospitals (Q826868) (← links)
- Mathematical optimization in classification and regression trees (Q828748) (← links)
- COVID-19 pandemic forecasting using CNN-LSTM: a hybrid approach (Q832776) (← links)
- Bootstrap estimated true and false positive rates and ROC curve (Q961178) (← links)
- Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography (Q961895) (← links)
- The Bayesian additive classification tree applied to credit risk modelling (Q962375) (← links)
- Editorial: Machine learning and robust data mining (Q1020795) (← links)
- A stochastic approximation view of boosting (Q1020818) (← links)
- Robust learning from bites for data mining (Q1020821) (← links)
- A local boosting algorithm for solving classification problems (Q1023522) (← links)
- A geometric approach to leveraging weak learners (Q1603593) (← links)
- Evaluating the importance of different communication types in romantic tie prediction on social media (Q1639257) (← links)
- Gradient boosting for high-dimensional prediction of rare events (Q1658126) (← links)
- Noise peeling methods to improve boosting algorithms (Q1660240) (← links)
- Multi-target regression via input space expansion: treating targets as inputs (Q1689552) (← links)
- Volterra equation based models for energy storage usage based on load forecast in EPS with renewable generation (Q1717125) (← links)
- Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S\&P 500 (Q1751873) (← links)
- Universal sieve-based strategies for efficient estimation using machine learning tools (Q1983607) (← links)
- Evaluating the impact of a HIV low-risk express care task-shifting program: a case study of the targeted learning roadmap (Q2001893) (← links)
- Determining cutoff point of ensemble trees based on sample size in predicting clinical dose with DNA microarray data (Q2013966) (← links)
- Scheduling many types of calibrations (Q2039673) (← links)
- Estimation of a density using an improved surrogate model (Q2044316) (← links)
- Consistent regression using data-dependent coverings (Q2044358) (← links)
- An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests (Q2066746) (← links)
- Interpretable machine learning: fundamental principles and 10 grand challenges (Q2074414) (← links)
- A new accelerated proximal boosting machine with convergence rate \(O(1/t^2)\) (Q2103099) (← links)
- Gradient boosting-based numerical methods for high-dimensional backward stochastic differential equations (Q2141183) (← links)
- The added value of dynamically updating motor insurance prices with telematics collected driving behavior data (Q2155841) (← links)
- Conclusive local interpretation rules for random forests (Q2172632) (← links)
- Cost-sensitive business failure prediction when misclassification costs are uncertain: a heterogeneous ensemble selection approach (Q2183867) (← links)
- Multi-output parameter-insensitive kernel twin SVR model (Q2185673) (← links)
- Interpretable regularized class association rules algorithm for classification in a categorical data space (Q2212562) (← links)
- A coarse-to-fine approach for intelligent logging lithology identification with extremely randomized trees (Q2238076) (← links)
- To imprison or not to imprison: an analytics model for drug courts (Q2241165) (← links)
